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Semiparametric linear regression with censored data and stochastic regressors

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorMora, Juan
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2009-04-15T06:44:55Z
dc.date.available2009-04-15T06:44:55Z
dc.date.issued1994-09
dc.description.abstractWe propose three new estimation procedures in the linear regression model with randomly-right censored data when the distribution function of the error term is unspecified, regressors are stochastic and the distribution function of the censoring variable is not necessarily the same for all observations ("unequal censoring"). The proposed procedures are derived combining techniques which produce accurate estimates with "equal censoring" with kernel-conditionalı Kaplan-Meier estimates. The performance of six estimation procedures (the three proposed methods and three alternative ones) is compared by means of some Monte Carlo experiments.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10016/3954
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries1994-31-12
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadística
dc.subject.otherCensoring
dc.subject.otherLinear regression
dc.subject.otherKaplan-Meier estimator
dc.subject.otherKernel estimator
dc.titleSemiparametric linear regression with censored data and stochastic regressors
dc.typeworking paper*
dspace.entity.typePublication
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